动力吸振器的多目标优化和多属性决策研究

在结构振动控制中,为了最大限度发挥吸振器的耗能减振作用.需要寻找吸振器的最优参数,即最优频率比、最优阻尼比和最优质量比,使得结构在不同的频率激励下获得最好的减振效果.本文将基于进化算法的多目标优化技术与多属性决策方法联合运用,针对主系统存在阻尼的减振系统,研究了动力吸振器的优化和决策同题.对于多目标优化问题,采用改进的非支配解排序的多目标进化算法(NSGA Ⅱ),求出Pareto最优解,由这些Pareto最优解构成决策矩阵,使用客观赋权的信息熵方法对最优解的属性进行权值计算.然后用逼近理想解的排序方法(TOPSIS)进行多属性决策(MADM)研究,对Pareto最优解给出排序.文中给出了4个设计参数、3个目标函数的动力吸振器优化设计算例.     

基于TOPSIS方法的自适应EWMA图多目标优化设计

针对自适应EWMA控制图的多目标优化设计问题,提出了一种基于三次函数的自适应EWMA图构造方法,将控制图的统计特性和经济特性同时作为目标函数,建立了自适应EWMA图的多目标优化设计模型,并基于TOPSIS方法给出了针对一个偏移区间的优化设计步骤。最后,将自适应EWMA控制图多目标优化设计方案与已有的自适应EWMA图统计性优化设计和EWMA图经济统计优化设计分别在统计特性和经济特性上进行比较,结果表明本文提出的优化设计方法在这两方面都具有良好的性能。     

基于差异演化算法的网络计划多目标优化

为了提高施工企业的经济效益，在综合考虑成本、质量和进度的基础上，提出了工期－净收益－质量多目标优化模型，并采用一种新颖的进化算法即差异演化算法对该模型进行求解，最后通过工程实例验证模型和算法的有效性。     

信息系统安全方案的多属性决策方法

为解决信息系统安全建设中信息安全方案的选择问题,综合分析信息安全方案对安全等级保护要求的符合程度、信息安全方案的采购成本、运行维护费用和信息安全方案对抗风险的效能等多种因素和指标,并根据这些因素对信息安全方案选择的影响,提出一种多属性决策方法。通过信息安全方案对抗风险的效能指数计算、信息安全方案各影响因素的权重赋值和信息安全方案综合指数的计算,依据综合指数对信息安全方案进行择优排序,实现对信息安全方案的恰当选择。对于符合基本条件（满足安全等级保护要求、成本在限制范围内、对抗风险达到要求等）而难以取舍的几个备选方案,该选择方法是合适和简捷的。     

考虑决策信息变化的多属性群决策方法研究

动态变化的实际决策环境使得决策者的决策偏好发生变化,进而使决策者在不同时间点的决策信息发生变化,从而影响决策结果。因此,考虑决策者信息变化的多属性群决策问题是一个值得关注的问题。进而提出了考虑决策信息变化的多属性群决策方法,以获得更加符合实际的评价结果。首先,基于专家共识最大化的原则,根据专家对决策单元属性的评价信息构建非线性规划模型求解专家权重,根据专家对决策单元属性评价信息的变化调整专家权重;其次,基于结构熵权法,根据专家对属性的排序信息确定属性权重,根据专家对属性重要性排序的变化调整属性权重,采用EDAS方法对决策单元进行排序。最后,采用供应商评价的算例验证了所提出方法的可靠性及有效性。     

基于多目标决策方法的反应离子刻蚀机约束环优化设计研究

从反应离子刻蚀设备腔室内部结构细节角度出发,研究腔室约束环对以450 mm为代表大晶圆尺寸刻蚀机流场影响。与同类研究相比,本文旨在突出研讨仿真模型的精确性与边界条件比较设置准则的重要性。本文首先介绍刻蚀机腔室约束环的结构及其功能并提出高精度粘度修正的实现方法并计算腔室流体气压分布。提出了腔室气压分布的多目标问题,采用遗传算法求解,并通过多目标决策对优化结果进行评价和排序,得到最适合的优化结果。最后,给出约束环设计结果并得出约束环可以优化腔室内气流分布轮廓的结论。     

广州可持续发展多目标决策支持算法研究

以广州市为例,采用广州市历史统计数据,对将来的发展进行预测。在此基础上,探讨人口、资源、环境与经济之间的关系,建立了可持续发展多目标决策支持模拟系统。模型的计算结果可供政府及高、中层决策者制定政策时作参考。     

基于信息熵多属性决策的物流供应商选择评价

生产商选择合适的物流供应商有利于降低营运成本,提高竞争优势.高效的第三方物流活动在增强企业核心竞争力、降低企业成本等方面起着重要作用.针对传统的熵值和熵权计算公式的不足,提出改进的熵权计算公式.同时把专家的主观权重和样本自身产生的熵权相结合,通过一定的方式混合加权作为不确定属性的权重.在此基础上结合多属性决策模型建立多属性决策评价方法.该方法将主、客观分析相结合,具有一定的优势：既减少了决策问题的主观性,又使得整个评价过程能够量化,有效解决了第三方物流供应商选择评价中的不确定多属性决策问题.最后通过实例分析证明该方法的可行性和有效性.     

平流层飞艇的多目标优化与决策

该文建立了以平流层飞艇阻力最小、自重最轻、极限承载力最大及刚度最大为优化目标的多目标优化模型;采用强度Pareto进化算法(SPEA)进行了多目标优化设计;基于优化所得的Pareto解集,采用基于信噪比的决策方法选择满足实际需要的最终方案。结果表明：采用的SPEA算法是合理有效的,可以得到非劣解分布较均匀的Pareto曲面;通过基于信噪比的决策方法,可从非劣解集中获得满足实际要求的最稳健设计方案。     

模糊多属性决策的维修服务质量评价

维修服务质量是不同属性的综合体，它包括许多很难被测量的不可感知属性。这种特性给被测者在接受问卷调查时带来了一些困难。为了避免上述情况的出现，运用模糊理论评价维修点的维修服务质量，建立了模糊多属性决策模型，并将模糊集理论引入不可感知属性指标值的测度中。用层次分析法获得准则的权重和用逼近理想解排序法进行维修服务质量的排序后，得到了满意的结果。     

Evaluating flexible manufacturing systems using a combined multiple attribute decision making method

This paper presents a logical procedure to evaluate alternative flexible manufacturing systems for a given industrial application. The procedure is based on a combined multiple attribute decision making method using TOPSIS and AHP methods together. A ‘flexible manufacturing system suitability index’ is proposed that evaluates and ranks flexible manufacturing systems for the given industrial application. The methodology is illustrated by means of an example.     

A two-stage approach for multi-objective decision making with applications to system reliability optimization

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.     

Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.     

Assembly line balancing by a new multi-objective differential evolution algorithm based on TOPSIS

In this paper, we propose a multi-objective differential evolution algorithm (MODEA) to solve the multi-objective simple assembly line balancing problem type-2 (SALBP-2). This problem arises when in an existing assembly line, changes in the production process or demand structure take place and the organisation wants to produce the optimum number of items using a fixed number of workstations, which is associated with optimally assigning the tasks to an ordered sequence of stations such that the precedence relations are not violated and some measures of performance are optimised. The two considered objectives are: minimising the cycle time and the smoothness index of the assembly line. To that purpose, we develop a MODEA which unlike the existing algorithms deals with the considered objectives separately in selecting the next population members by proposing a new acceptance scheme based on the Pareto dominance concept and a new evaluation scheme based on TOPSIS. Also, by using the Taguchi method, we tune the effective factors of the developed algorithm. Then its efficiency is tested over available assembly line balancing benchmarks and compared to a new algorithm provided recently in the bi-objective SALBP-2 literature. Computational experiments indicate that the developed algorithm outperforms the existing meta-heuristic over a large group of benchmarks.     

An entropy-based three-stage approach for multi-objective system reliability optimization considering uncertainty

This article presents a three-stage approach for solving multi-objective system reliability optimization problems considering uncertainty. The reliability of each component is considered in the formulation as a component reliability estimate in the form of an interval value and discrete values. Component reliability may vary owing to variations in the usage scenarios. Uncertainty is described by defining a set of usage scenarios. To address this problem, an entropy-based approach to the redundancy allocation problem is proposed in this study to identify the deterministic reliability of each component. In the second stage, a multi-objective evolutionary algorithm (MOEA) is applied to produce a Pareto-optimal solution set. A hybrid algorithm based on k-means and silhouettes is performed to select representative solutions in the third stage. Finally, a numerical example is presented to illustrate the performance of the proposed approach.     

Fuzzy multiple attribute decision-making method for a large 300-mm fab layout design

The trend in design of 300-mm wafer fabrication facilities is to seek means to bring about economies of scale. However, this may require trade-offs with various operational efficiencies. This research explores 10 alternative layout designs for a large 300-mm fabrication facility. These alternatives are the different combinations of independent and layer-based cellular design concepts, and are evaluated using a fuzzy multiple attribute decision-making method. A case study has been adopted for an empirical illustration. The results show that a megafab is not a preferred choice. The analysis concludes that a moderate-sized independent cell is a preferred design concept. The results provide fabrication facility management with guidelines for a future 300-mm layout design, which is the backbone of a cost-intensive 300-mm fabrication facility. Future research opportunities are also addressed.     

Practical solutions for multi-objective optimization: An application to system reliability design problems

For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her objective function priorities. In the second approach, we used data mining clustering techniques to group the data by using the k-means algorithm to find clusters of similar solutions. This provides the decision maker with just k general solutions to choose from. With this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker. These are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set.     

基于公理设计和相容决策支持问题法的稳健优化设计方法

 针对多目标稳健优化问题,建立了多目标稳健优化的损失函数,利用灵敏度分析方法确定各设计变量对各设计目标的影响程度,确定主要的设计参数,便于调整和控制设计参数的公差.根据信息公理与损失函数的一致性关系,建立以最小化各目标的总损失函数为目标函数.并在相容决策支持问题法框架基础上,提出一种基于公理设计和相容决策支持问题法的多目标稳健优化设计模型.实例分析表明,提出的方法是可行的.     

基于模糊设计准则的机械可靠性优化设计研究

按照应力-强度分布干涉和模糊理论,对机械模糊可靠性优化设计方法进行系统的研究,提出了剩余可靠度的概念,建立了数学模型,给出了设计实例。研究表明：仅仅以实现要求的可靠度为目标的一般模糊可靠性设计由于不能考虑其他条件对设计变量取值的约束作用,往往导致设计结果不具可行性而失去实际意义。可靠性优化设计将模糊可靠性条件与其他所有限制条件一起作为设计约束,在设计空间围成一个可行设计域,然后按照设计目标要求用一定方法寻求可行域内的最优设计点,从而保证了设计结果既具可靠性,又具可行性和最优性。     

基于蚁群算法的多值属性系统测试序列优化

针对系统故障诊断中的多值属性系统测试序列优化问题,该文提出一种改进的蚁群算法,将成功运用在二值属性系统中的蚁群算法扩展到多值属性系统中,根据多值属性系统特点,设计相应的状态转移规则和信息素更新机制,并采用蚁群算法和遗传算法相融合的联合优化策略,解决了多值属性系统的序列优化问题,为多值属性系统的测试优化问题提供了一条新的解决途径。